Top Tech Jobs & Startup Jobs

Leads architecture, design, and implementation of secure global on-premises, hybrid cloud, and air-gapped DoD networks. Responsibilities include advanced routing, high availability, datacenter infrastructure, cloud connectivity, KG encryption integration, documentation, troubleshooting, and technical mentorship.

Architect, deploy, secure, and maintain scalable Kubernetes platforms and clusters. Manage networking, storage, scheduling, RBAC, Operators, containerized workloads, Infrastructure as Code, CI/CD pipelines, and cloud-native automation. Build operational tools using Python, Go, or Bash, mentor development teams, evaluate Kubernetes solutions, and promote GitOps and AI-assisted development practices. The role requires an active TS/SCI clearance and DoD 8570/8140 certifications.

Support and maintain cloud-based and on-premises DevOps environments across AWS, Azure, and hybrid infrastructure. Responsibilities include Kubernetes administration, application deployment, monitoring, troubleshooting, patching, identity management, automation, infrastructure-as-code, capacity planning, networking, and support for systems operating across multiple classification levels.

Lead end-to-end SAP S/4HANA data migration across functional areas. Develop migration strategies, mappings, transformation rules, validation logic, templates, and load sequences. Execute data extraction, cleansing, transformation, loading, mock loads, cutover, reconciliation, and hypercare using SAP and ETL tools. Coordinate with functional, technical, data governance, and business teams; resolve load failures; support integrations, testing, and data quality initiatives.

Build, deploy, and maintain production machine learning models and end-to-end ML pipelines. Develop MLOps frameworks with model versioning, CI/CD, registries, feature stores, monitoring, and automated retraining. Deploy solutions using cloud and containerized technologies, troubleshoot performance issues, address model drift, and collaborate with engineering and data teams on scalable mission-critical applications.

Build and operate secure, production-grade generative AI applications for federal missions. Responsibilities include developing agentic workflows and RAG systems, integrating cloud AI services, evaluating models, designing prompts and guardrails, implementing observability and SRE practices, optimizing cost and latency, and delivering reusable SDKs, CI/CD templates, Terraform modules, and evaluation tooling for restricted or air-gapped environments.

Leads an application security scrum team and integrates security throughout software development. Responsibilities include vulnerability testing and remediation, securing CI/CD pipelines, threat modeling web and AI applications, managing SBOMs and dependencies, integrating security tools, verifying and signing artifacts, and guiding developers on secure engineering practices in a federal environment.

Provides application security engineering and sustainment throughout system lifecycles. Responsibilities include administering, configuring, tuning, monitoring, and securing applications; supporting ISSOs with ATO/ATT documentation; integrating AI and ML into security tools; conducting security impact assessments; managing configurations and changes; ensuring logging compliance; and supporting vulnerability management. Requires significant cybersecurity-related technical experience and a TS/SCI clearance.

Build, automate, secure, and operate Google Cloud infrastructure and platform capabilities. Develop infrastructure as code, CI/CD pipelines, containerized workloads, observability, reliability, security, and automation practices. Troubleshoot incidents, collaborate with engineering and security teams, and progressively lead platform initiatives, establish standards, mentor engineers, and influence cloud architecture.

Designs and operates AI agent systems by translating user stories into prompts, architecting subagents, and building routing and escalation logic. Manages generative AI pipelines, prompt libraries, RAG knowledge bases, CI/CD integration, security validation, and ATO evidence generation. Prototypes AI workflows, analyzes context and structured data, documents practices, and trains team members while collaborating with AI product, engineering, and verification teams.